Jia-Jyun Dong, Yu-Hsiang Tung , Jia-Jyun Dong, Yu-Hsiang Tung , Chien-Chih Chen, Jyh-Jong Liao , Chien-Chih Chen, Jyh-Jong Liao , Yii-Wen Pan Yii-Wen Pan

學 生 ：陳奕愷學 生 ：陳奕愷

Logistic regression model for Logistic regression model for predicting the failure probability predicting the failure probability

of a landslide damof a landslide dam

OUTLINEOUTLINE

Introduction

Methodology

Results

Conclusions

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IntroductionIntroduction

• After the formation of a landslide dam, the natural lake may bequickly breached with an outburst flood and debris flow that resultsin a catastrophic disaster.

• Rapid assessment of the landslide-dam stability is one of the crucial steps for decision-making to reduce the related disasters. Geomorphic approaches are widely used to correlate the dam, river, impoundment characteristics and landslide-dam stability.

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IntroductionIntroduction

DBI

43 well documented landslide dams

find out the dominant variables

stability of a landslide dam and to construct a series of multivariate regression models

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IntroductionIntroduction

Discriminant models PHWL

Ds = -2.94log(A) -4.58log(H) +4.17log(W) +2.39log(L) -2.52

Discriminant models AHWL

Ds = -2.62log(A) -4.67log(H) +4.57log(W) +2.67log(L) +8.26

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IntroductionIntroduction

Ds is the discriminant score; P, H,W, L, A are the Peak flow, dam height, width, length and catchment area, respectively.

Discriminant models AHV

Ds = -2.13log(A) -4.08log(H) +2.94log(V) +4.09

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IntroductionIntroduction

• The risk assessment and management may involve (1) Estimating the risk level (2) judging whether the risk level acceptable (3) exercising appropriate countermeasures to reduce the risk

• This work also compared the performance of the logistic regression models with those of the previous DBI index-based graphic model and discriminant models.

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MethodologyMethodology

• Logistic regression is useful when the dependent variable is categorical (e.g., presence or absence) and the explanatory (independent) variables are categorical, numerical, or both.

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MethodologyMethodology(Categorization of dataset by logistic regression analysis)(Categorization of dataset by logistic regression analysis)

Probability of a landslide dam remaining stable ：

Certain linear combination of the influencing variables：

LsePs

1

1

)1

ln(...110Ps

PsxbxbbLs nn

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MethodologyMethodology((Performance evaluation of the logistic regression model)Performance evaluation of the logistic regression model)

(1) The training set (17 unstable and 5 stable)(2) The target set (17 unstable and 4 stable)

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MethodologyMethodology((Performance evaluation of the logistic regression model)Performance evaluation of the logistic regression model)

• By using the target set, we then evaluated the predictive success of the model built on the training set.

• The prediction performance of a predictive model can be evaluated via the ROC (relative operating characteristic) diagram. The ROC diagram method has been widely used to measure the prediction potential of landslide susceptibility models.

• A larger area under the ROC Curve (AUC) indicates better model prediction;the index AUC ranges from 0.5 (for models with no predictive capability) to 1.0 (for models with perfect predictive power).

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MethodologyMethodology(Jack-knife technique)(Jack-knife technique)

• The jack-knife technique can be utilized to sort the importance of the relevant variables in the logistic regression model. We eliminate the variables in the logistic regression model one by one.

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ResultsResults((Logistic regression models PHWL_Log and AHWL_Log)Logistic regression models PHWL_Log and AHWL_Log)

Logistic regression models PHWL_Log

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ResultsResults((Logistic regression models PHWL_Log and AHWL_Log)Logistic regression models PHWL_Log and AHWL_Log)

Logistic regression models AHWL_Log

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ResultsResults((Logistic regression models PHWL_Log and AHWL_Log)Logistic regression models PHWL_Log and AHWL_Log)

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ResultsResults((Logistic regression models PHWL_Log and AHWL_Log)Logistic regression models PHWL_Log and AHWL_Log)

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ResultsResults((Logistic regression models PHWL_Log and AHWL_Log)Logistic regression models PHWL_Log and AHWL_Log)

• The overall prediction power (success rate) of AHV_Log was 89.3%.The cross-validation accuracy was 85.7%. The AUC of the Model AHV_Log was 0.951.

• Comparing with the overall prediction powers of 70.1% and 64.9%,for the discriminant model AHV_Dis and for the index-based graphic model (DBI<2.75 stable; DBI>3.08 unstable), the logistic regression model seems to have a better ability to categorize landslide dams (as stable or unstable).

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ResultsResults((Importance of the factors affecting landslide-dam stabilityImportance of the factors affecting landslide-dam stability))

• The jack-knife technique was utilized to examine the relative importance of each variable in the predictive models. We eliminated one of the four variables one by one and established four logistic regression sub-models.

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ResultsResults((Importance of the factors affecting landslide-dam stabilityImportance of the factors affecting landslide-dam stability))

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ResultsResults((Failure probability of landslide dams in Tobata inventoryFailure probability of landslide dams in Tobata inventory))

About 87% of unstable landslide dams had a failure probability greater than 80%

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ResultsResults((Failure probability of landslide dams in Tobata inventoryFailure probability of landslide dams in Tobata inventory))

About 85% of unstable landslide dams had a failure probability greater than 80%

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ResultsResults((Failure probability of landslide dams in Tobata inventoryFailure probability of landslide dams in Tobata inventory))

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ResultsResults((Application to the landslide dams induced by recentApplication to the landslide dams induced by recent catastrophic eventscatastrophic events))

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ConclusionsConclusions

• The proposed models PHWL_Log and AHWL_Log were able to categorize the landslide dams into stable and unstable groups with high success rates.

• Model PHWL_Log (AUC=94.8%) was slightly superior to model AHWL_Log (AUC=92.5%). Yet, model AHWL_Log may be more useful in practice because peak flow information is not always available in the early stage after dam formation.

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ConclusionsConclusions

• The log-transformed peak flows are identified as the most important geomorphic variables influencing the stability of a landslide dam. The log-transformed dam height, with a negative contribution to the stability of a landslide dam,is the second most significant variable.

• In addition to the classification of landslide dams into the stable and unstable groups, the failure probability can also be evaluated based on the proposed logistic regression models.

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ConclusionsConclusions

• The failure probability of an earthquake-induced landslide dam decreased if the prediction model AHWL_Log was replaced by model PHWL_Log.

• The failure probability of a heavy rainfall-induced case predicted by model PHWL_Log is higher than that by AHWL_Log because a high flow rate is incorporated into the prediction model (PHWL_Log).

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ConclusionsConclusions

• It would be more suitable if both the failure probability and the impact due to the outburst flood from the landslide-dammed lake could be considered separately for classifying the risk level of a landslide dam.

• A simple model describing the failure probability of landslide dams, such as the proposed logistic regression model,is necessary for classifying the risk level related to a landslide dam breach.

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ConclusionsConclusions

• The proposed models can be used for evaluating the risk associated with outburst floods from landslide-dammed lakes.These models can be used as an evaluation tool for decision-making concerning hazard mitigation actions, especially when the allowable time is limited.

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